A roadmap for enriching rare cell populations from human post-mortem brain, demonstrated by 100% microglial purity (scRNA-Seq)
Single-cell RNA-seq · human · 18 samples
- What it is
- Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
- Organism
- Homo sapiens (human)
- Samples
- 18 samples: 17 distinct titles, 1 of them repeatedGroups are read from the sample names, not from the paper. Check the list below.
- Data you can get
- A processed table (series matrix) and supplementary files from NCBI; the raw sequencing reads from ENA. Which of these hold counts depends on the study, so check the file list.
Read the full summary ↓
Direct links to NCBI and ENA, no account and no request form. Pick by what you want to do: take the small processed table to look, the supplementary files to re-analyse, or the original raw data to start over.
Processed table
The series matrix: one table of values per sample as the submitters processed it. The smallest download, and it opens in Excel or R. Start here to see what the data look like.
Supplementary files
The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE333448_RAW.tar (it can run to tens of gigabytes).
Raw sequencing reads
The original FASTQ files, straight from the sequencer, held at ENA. Large, and you align them yourself, so only choose this if you need to re-process the study from scratch.
What the files usually look like: Single-cell studies usually ship their counts as 10x files (matrix.mtx, barcodes.tsv, features.tsv), an .h5 file, or an .rds / .h5ad object that Seurat or Scanpy can open. Look for those among the supplementary files.
Also filed as BioProject PRJNA1469237. Searching any of these in the dataset finder brings you back here.
How we read this: Read from 18 sample titles: 17 distinct titles with little repetition. These groups are guessed from the sample names, not taken from the paper, so check them against the sample list below before relying on them.
- GSM9765044 snRNA-seq of post-mortem human parietal cortex, NeuN-Olig2-IRF8+
- GSM9765045 snRNA-seq of post-mortem human occipital cortex, NeuN-Olig2-IRF8+ batch1
- GSM9765046 snRNA-seq of post-mortem human occipital cortex, NeuN-Olig2-IRF8+ batch2
- GSM9765047 snRNA-seq of post-mortem human occipital cortex, NeuN-Olig2-PU1+
- GSM9765048 snRNA-seq of post-mortem human occipital cortex, NeuN-Olig2-PU1+Sox9-
- GSM9765049 snRNA-seq of post-mortem human occipital cortex, NeuN-Olig2-IRF8+ replicate
- GSM9765050 snRNA-seq of post-mortem human cerebellum, NeuN-Olig2-PU1+IRF8+Sox9-
- GSM9765051 snRNA-seq of post-mortem human occipital cortex, NeuN-Olig2-Sox9+
- GSM9765052 snRNA-seq of post-mortem human occipital cortex, NeuN+Olig2-PU1-IRF8-Sox9-
- GSM9765053 snRNA-seq of post-mortem human occipital cortex, NeuN-Olig2+PU1-IRF8-Sox9-
- GSM9765054 snRNA-seq of post-mortem human occipital cortex, NeuN-Olig2-PU1-IRF8-Sox9+
- GSM9765055 snRNA-seq of post-mortem human occipital cortex, NeuN-Olig2-PU1+IRF8+Sox9-
- GSM9765056 snRNA-seq of post-mortem human occipital cortex, NeuN-Olig2-PU1-IRF8-Sox9-
- GSM9890436 snRNA-seq of post-mortem human parietal cortex white matter, NeuN+Olig2-PU1-IRF8-Sox9-
- GSM9890437 snRNA-seq of post-mortem human parietal cortex white matter, NeuN-Olig2+PU1-IRF8-Sox9-
- GSM9890438 snRNA-seq of post-mortem human parietal cortex white matter, NeuN-Olig2-PU1+IRF8+Sox9-
- GSM9890439 snRNA-seq of post-mortem human parietal cortex white matter, NeuN-Olig2-PU1-IRF8-Sox9+
- GSM9890440 snRNA-seq of post-mortem human parietal cortex white matter, NeuN-Olig2-PU1-IRF8-Sox9-
- GSE225331 Profiling cancer cells during early metastasis identifies targetable subpopulations [cell line scRNA-seq] 1238 samples shares populations, scrna-seq
- GSE270894 Molecular Characterization of Chemoresistant Cell Populations Improves Risk Stratification and Therapy Response Prediction in Pediatric AML [pAML Patients scRNA-seq] 20 samples shares populations, scrna-seq
- GSE319493 Phenotypically similar but functionally distinct NK cell populations within the human maternal-fetal interface [scRNA-seq] 15 samples shares populations, scrna-seq
- GSE263707 Targeting signaling rewiring and resistant subpopulations in Philadelphia Chromosome-like Acute Lymphoblastic Leukemia [scRNA-seq] 14 samples shares populations, scrna-seq
- GSE271136 Molecular Characterization of Chemoresistant Cell Populations Improves Risk Stratification and Therapy Response Prediction in Pediatric AML [pAML PDX scRNA-seq] 12 samples shares populations, scrna-seq
- GSE286148 Two parallel neural ectoderm populations contribute to the developing brain [scRNA-seq] 6 samples shares populations, scrna-seq
- GSE301390 Prediction and Characterization of Genetically-Regulated Expression of Asthma Tissues from African-Ancestry Populations 260 samples shares populations
- GSE308462 ALS molecular subtypes are a combination of cellular, genetic, and pathological features learned by deep multiomics classifiers (post-mortem cortex) 260 samples shares post-mortem
Metadata from NCBI GEO, cached and refreshed periodically — the NCBI page above is authoritative. Downloads link straight to NCBI/ENA; nothing is proxied through BioTransfer.